Functional Data Analysis with R and MATLAB - James Ramsay.pdf

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Use R!
Series Editors:
Robert Gentleman Kurt Hornik Giovanni Parmigiani
For other titles published in this series, go to
http://www.springer.com/series/6991
J.O. Ramsay Giles Hooker Spencer Graves
.
.
Functional Data Analysis
with R and MATLAB
J.O. Ramsay
2748, Howe Street
Ottawa, ON K2B 6W9
Canada
ramsay@psych.mcgill.ca
Giles Hooker
Department of Biological Statistics
& Computational Biology
Cornell University
1186, Comstock Hall
Ithaca, NY 14853
USA
gjh27@cornell.edu
Spencer Graves
Productive Systems Engineering
751, Emerson Ct.
San Jose, CA 95126
USA
spencer.graves@prodsyse.com
Series Editors:
Robert Gentleman
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer
Research Center
1100, Fairview Avenue, N. M2-B876
Seattle, Washington 98109
USA
Kurt Hornik
Department of Statistik
and Mathematik
Wirtschaftsuniversität
Wien Augasse 2-6
A-1090 Wien
Austria
Giovanni Parmigiani
The Sidney Kimmel
Comprehensive Cancer Center
at Johns Hopkins University
550, North Broadway
Baltimore, MD 21205-2011
USA
ISBN 978-0-387-98184-0
e-ISBN 978-0-387-98185-7
DOI 10.1007/978-0-387-98185-7
Springer Dordrecht Heidelberg London New York
Library of Congress Control Number: 2009928040
©
Springer Science+Business Media, LLC 2009
All rights reserved. This work may not be translated or copied in whole or in part without the written
permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY
10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection
with any form of information storage and retrieval, electronic adaptation, computer software, or by similar
or dissimilar methodology now known or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are
not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject
to proprietary rights.
Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)
Preface
This contribution to the useR! series by Springer is designed to show newcomers
how to do functional data analysis in the two popular languages, Matlab and R. We
hope that this book will substantially reduce the time and effort required to use these
techniques to gain valuable insights in a wide variety of applications.
We also hope that the practical examples in this book will make this learning
process fun, interesting and memorable. We have tried to choose rich, real-world
problems where the optimal analysis has yet to be performed. We have found that
applying a spectrum of methods provides more insight than any single approach by
itself. Experimenting with graphics and other displays of results is essential.
To support the acquisition of expertise, the “scripts” subdirectory of the com-
panion fda package for R includes files with names like “fdarm-ch01.R”, which
contain commands in R to reproduce virtually all of the examples (and figures)
in the book. This can be found on any computer with R and fda installed using
system.file(’scripts’, package=’fda’).
The Matlab code is pro-
vides as part of the fda package for R. From within R, it can be found us-
ing
system.file( ’Matlab’, package=’fda’).
It also can obtained by
downloading the
.tar.gz
version of the fda package for R from the Compre-
hensive R Archive Network (CRAN,
www.r-project.org),
unzipping it and
looking for the
inst/Matlab
subdirectory.
The contents of a book are fixed by schedules for editing and printing. These
script files are not similarly constrained. Thus, in some cases, the script files may
perform a particular analysis differently from how it is described in the book. Such
differences will reflect improvements in our understanding of preferred ways of
performing the analysis described in the book. The web site www.functionaldata.org
is a resource for ongoing developments of software, new tools and current events.
The support for two languages is perhaps a bit unusual in this series, but there
are good reasons for this. Matlab is expensive for most users, but its for capacity
modeling dynamical systems and other engineering applications has been critical in
the development of today’s fda package, especially in areas such chemical engineer-
ing where functional data are the rule rather than the exception and where Matlab is
widely used. On the other hand, the extendibility of R, the easy interface with lower-
v

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